Update README.md

README.md

@@ -83,13 +83,26 @@ More informatation can be found in our paper [NUTSHELL: A Dataset for Abstract G
 
 ## Citation
 ```
-@misc{züfle2025nutshelldatasetabstractgeneration,
-      title={NUTSHELL: A Dataset for Abstract Generation from Scientific Talks}, 
-      author={Maike Züfle and Sara Papi and Beatrice Savoldi and Marco Gaido and Luisa Bentivogli and Jan Niehues},
-      year={2025},
-      eprint={2502.16942},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2502.16942}, 
+@inproceedings{zufle-etal-2025-nutshell,
+    title = "{NUTSHELL}: A Dataset for Abstract Generation from Scientific Talks",
+    author = {Z{\"u}fle, Maike  and
+      Papi, Sara  and
+      Savoldi, Beatrice  and
+      Gaido, Marco  and
+      Bentivogli, Luisa  and
+      Niehues, Jan},
+    editor = "Salesky, Elizabeth  and
+      Federico, Marcello  and
+      Anastasopoulos, Antonis",
+    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
+    month = jul,
+    year = "2025",
+    address = "Vienna, Austria (in-person and online)",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.iwslt-1.2/",
+    doi = "10.18653/v1/2025.iwslt-1.2",
+    pages = "19--32",
+    ISBN = "979-8-89176-272-5",
+    abstract = "Scientific communication is receiving increasing attention in natural language processing, especially to help researches access, summarize, and generate content. One emerging application in this area is Speech-to-Abstract Generation (SAG), which aims to automatically generate abstracts from recorded scientific presentations. SAG enables researchers to efficiently engage with conference talks, but progress has been limited by a lack of large-scale datasets. To address this gap, we introduce NUTSHELL, a novel multimodal dataset of *ACL conference talks paired with their corresponding abstracts. We establish strong baselines for SAG and evaluate the quality of generated abstracts using both automatic metrics and human judgments. Our results highlight the challenges of SAG and demonstrate the benefits of training on NUTSHELL. By releasing NUTSHELL under an open license (CC-BY 4.0), we aim to advance research in SAG and foster the development of improved models and evaluation methods."
 }
 ```